Cotton trash is any non-lint material in the cotton. Examples of cotton trash are leaf, bark, grass and small foreign particles known as pepper trash. The amount of trash in cotton bales is a crucial factor in determining the market value of the cotton. The value used in determining cotton market value is classer's Leaf Grade and Bark/Grass Grade. The kind of trash and an indication of the amount (the Leaf Grade “1” through “7” and the Bark/Grass Grade “0”, “1” or “2”) are visually estimated by a human classer. Classer determination for leaf grade and bark/grass grade has been used for many decades and is still part of the official U.S. Department of Agriculture (USDA) cotton classification. No automatic system is known to be used to determine leaf grade and bark/grass grade currently.
Although the USDA provides High Volume Instrument (HVI) trash measurement, the correlation between HVI trash and classer's Leaf Grade is very low, and it is impossible to get Bark/Grass Grade from the HVI trash measurement. HVI trash is a measure of the amount of non-lint materials in the cotton, such as leaf, bark and grass. HVI trash measurement systems analyze the gray-scale images of a cotton sample surface using a fixed intensity threshold to identify pixels in cotton image as “lint” or “non-lint”. The amount of trash is then quantified as the percentage of the surface area occupied by non-lint pixels. While this method performs well if the cotton is bright white in color, the trash particles are always much darker than the lint, and the image light source does not degrade with use, these assumptions are often not satisfied in practice. Cotton colors range from bright white to dark yellow, non-lint material varies in darkness, and light sources degrade with use. Thus, it is not possible to assign a single threshold that is able to accurately discriminate between lint and non-lint material under all commonly encountered conditions. On the other hand, since all pixels that are darker than a certain threshold are considered as trash, the HVI trash system can not distinguish leaf, bark and grass, even shadows. That makes HVI trash system can not make good leaf grade and is impossible to make bark/grass grade.
The major advantage of human classing of cotton lies in the capability of a human being learning, organizing and accumulating knowledge. An experienced human classer can do a better job of categorizing and grading the cotton than any heretofore known computer system. However, the disadvantages are rather obvious. It is much more expensive to train and maintain a human classing system than a computer classing system. The reproducibility between two different individuals is lower compared to reproducibility between multiple computer systems. Moreover, a human classer can not quickly make accurate measurement of such as things as particle count, percentage area occupied by trash and average size of the trash particle. Thus, industry demands an automatic, accurate and reliable trash measurement system to replace the traditional human classing system for cotton.
Many attempts have been made to track a human classer's leaf grade based on percentage of surface area occupied by trash particles or count of trash particles, but all have failed. A human classer is trained using USDA leaf standard and a large number of cotton samples with known leaf and bark/grass grade. An experienced human classer forms a visual image of a cotton sample and usually determines leaf and bark/grass grade within few seconds for each sample. They never actually count the particles in the cotton sample or measure the size of the particles. What they do is first to categorize the content in the cotton sample to several categories and estimate the amount of each category based upon their image of the sample as compared to their “learned” standards. Second, they mentally process the information and make a leaf and bark/grass grade according to the knowledge that they learned through training and experience.
A successful non-human trash measurement system desirably would be able to emulate the human classer's system. First, the system must have the ability to categorize the content of the cotton image. There are lint, leaf, bark, grass, and shadows in a sample image. The system must be able to categorize the pixels in the cotton image into these categories and compute the amount of each category.
Second, the system must have the ability to implement and maintain a mathematical model that is able to learn the knowledge and process the multiple dimensional information non-linearly.
Image segmentation is the process of dividing images into sets of coherent regions or objects. It is one of the most difficult tasks in computer vision. It plays a critical role in object recognition of natural images. Unsupervised classification, or clustering, represents one promising approach for solving the image segmentation problem in typical application environments. The K-Means and Bayesian Learning algorithm are two well-known unsupervised classification methods. The K-Means approach is computationally efficient but assumes imaging conditions which are unrealistic in many practical applications. While the Bayesian learning technique always produces a theoretically optimal segmentation result, the large computational burden it requires is often unacceptable for many industrial tasks. A novel clustering algorithm, called Bayesian Weighted K-Means, is developed to divide the cotton image into lint and non-lint regions in trash measurement system. Through simplification of the Bayesian learning approach's decision-making process using cluster weights, the new technique is able to provide approximately optimal segmentation results while maintaining the computational efficiency generally associated with the K-Means algorithm.
Artificial Neural Network is an information-processing paradigm. It is based on the processing/memory abstraction of human information processing. Artificial neural network is composed of a large number of highly interconnected neurons that are connected with weighted connections. Each neuron is a mathematical model that emulates some of the features of biological neuron such as biological learning. Artificial Neural Network iteratively adjusts the connection weights based on a training set to learn the knowledge that is necessary to solve specific problems. The high non-linear structure and the capability of learning make the Artificial Neural Network well suit the problem that people are good at solving, but for which traditional methods are not. Artificial Neural Networks are used in trash measurement system to solve the leaf and bark/grass grading problems.